Democratic resilience is as much about the narratives of our nation we affirm, as the institutions that enshrine our values and laws, a fact re-affirmed by scholarship across many branches of social science in recent decades. For policymakers and quantitative social scientists, analysing or tracking public discourse through the lens of narrative and framing has historically involved the annotation of texts by hand, placing severe limitations on the scale and modality of discourse under inquiry. Yet, a revolution is at hand—a transformer revolution: first arising in computer science, and now enabling a new kind of automated narrative analysis at scale, transformers are opening up new horizons for the tracking of public narratives of democratic resilience. Here, we: formulate a conceptual framework linking computational language methods to democratic resilience analysis; introduce transformer-based artificial intelligence (AI) methods as a third wave in natural language processing technology; and demonstrate two practical applications of transformer methods to democratic narrative analysis. Finally, we conclude by contributing data and research recommendations which flow naturally from the opportunities unlocked by transformer methods for public stakeholders who wish to see these opportunities realised. Together, we suggest that, perhaps for the first time, the “holy grail” of the quantitative social scientist is near: the ability to identify, accurately, and efficiently, nuanced narratives in text, at scale.

Indonesia’s rapid digital transformation generates massive economic opportunities while posing new regulatory challenges. This study examines how Indonesia’s digital economy is governed through key regulatory domains: electronic commerce, data governance, and digital taxation, and assesses their impact on inclusive growth. The research utilizes an integrated theoretical lens, Institutional Theory and Digital Divide Theory, to analyze how formal rules and on-the-ground access to technology together shape outcomes. The research employs a multimethod approach, to understand how different regulatory models influence market participation, business compliance, and digital inclusion. The findings reveal significant regional disparities in internet access and digital business adoption, with rural and remote areas lagging behind urban centers. Micro, small, and medium-sized enterprises face disproportionate compliance burdens under current regulations, a gap partly addressed through cooperative initiatives like the Indonesia–Japan Track 1.5 Public–Private Partnership. A key insight is the complex impact of data localization policies: while strengthening digital sovereignty and user privacy, strict localization requirements can raise operational costs and deter foreign investment. The paper concludes with policy recommendations for improving regulatory implementation, expanding digital infrastructure and literacy, and fostering international cooperation to ensure Indonesia’s digital economy regulations promote equitable growth across its diverse archipelago.

Citizen-generated data (CGD) is increasingly embraced as a strategy for filling data gaps to achieve the United Nations Sustainable Development Goals (SDGs), including SDG 5, Gender Equality and the Empowerment of Women and Girls. Existing frameworks to guide the design and use of CGD, however, do not reflect the unique considerations of CGD projects addressing issues of gender inequality. Answering recent calls for study of “data practices,” this article analyzes common CGD principles through findings from an action research project to address gender-based violence at the Colombia–Venezuela border. We suggest that while existing frameworks provide generative pathways forward, several principles around which consensus appears to be emerging are too rigid or insufficiently nuanced to account for the dynamics that many gender-focused CGD projects confront. These include dynamics such as physical security, the role of emotion in shaping project implementation, unequal access to resources, and political will. We suggest that this rigidity truncates the utility of these frameworks for CGD actors navigating highly sensitive issues in risky environments where serious violations of women’s human rights are taking place—and where the generation of gender data is one of several motivating factors for the work being done. This article reads gender into these frameworks to broaden the range of sustainable development issues for which CGD can help catalyze progress.

Web-enabled large language models (LLMs) frequently answer queries without crediting the web pages they consume, creating an “attribution gap” in responsible artificial intelligence (AI) usage—defined as the difference between relevant URLs read and those actually cited. Drawing on approximately 14,000 real-world LMArena conversation logs with search-enabled LLM systems, we document three exploitation patterns: (1) no search: 34% of Google Gemini and 24% of OpenAI GPT-4o responses are generated without explicitly fetching any online content; (2) no citation: Gemini provides no clickable citation source in 92% of answers; (3) high-volume, low-credit: Perplexity’s Sonar visits approximately 10 relevant pages per query but cites only three to four. A negative binomial hurdle model shows that the average query answered by Gemini or Sonar leaves about three relevant websites uncited, whereas GPT-4o’s tiny uncited gap is best explained by its selective log disclosures rather than by better attribution. Citation efficiency—extra citations provided per additional relevant web page visited—varies widely across models, from 0.19 to 0.45 on identical queries, underscoring that retrieval design, not technical limits, shapes ecosystem impact. To advance auditing and monitoring of AI systems, we recommend a transparent LLM search architecture based on standardized telemetry and full disclosure of search traces and citation logs.

Greenwashing poses a significant challenge to the fight against climate change by undermining trust in corporate sustainability claims. This study introduced the greenwashing tendency score (GTS), an automatable method designed to detect greenwashing tendencies in corporate sustainability reports. By leveraging textual sentiment and alignment analysis techniques in conjunction with environmental, social, and governance ratings, the GTS quantifies discrepancies between communicated and actual sustainability performance. We applied our methodology to 36 German stock index companies during the years from 2020 to 2022. Our key findings reveal substantial variations in greenwashing tendencies among these companies, emphasizing the need for more transparent and reliable sustainability reporting. The GTS emerged as a scalable, reproducible, and objective tool that can aid, for example, investors, regulators, and Non-government organizations in identifying greenwashing practices. This research contributed to the sustainable finance literature by introducing a neutral and open measure to assess firms’ greenwashing tendency, summarizing implications for policymaking and regulatory authorities and discussing its potential for long-term accountability and integrity in corporate sustainability communications.

As a major metropolitan city, London faces persistent road congestion and severe air pollution. To address these issues, static electronic road pricing (ERP) models have been implemented. While effective, these are inherently limited in flexibility. This paper explores dynamic ERP models to improve upon static pricing by minimizing air pollution and traffic congestion within the Congestion Charge Zone. The problem is formulated as a multi-stakeholder multi-objective optimization problem, incorporating the perspectives of three stakeholders—the government, vehicle owners, and environmental organizations—and three objectives: air pollution, traffic congestion, and price. The NSGA-II optimization algorithm was applied on a representative day and demonstrated substantial improvements. The concentration of PM$ {}_{2.5} $—the more harmful pollutant—was reduced by up to 23%, while NO2 levels fell by 2–3%. Traffic flow, used as a proxy for congestion, decreased by approximately 3–4% during peak hours. These improvements were achieved with only a modest increase in the mean price to £12.51 (from a baseline of £11.50), with a standard deviation of £1.59 and a variance of £2.43 across hourly prices. These results suggest that targeted dynamic pricing—when aligned with environmental and behavioural incentives—can deliver measurable gains in urban air quality and congestion without imposing a significant cost burden on drivers. A core novelty of this work lies in its practical, stakeholder-inclusive problem formulation. While the approach assumes infrastructure for automated price deduction and routing, this limitation can be addressed in future work through advances in vehicle–infrastructure communication systems.

The role of data and automated (non-artificial intelligence [AI]) algorithmic targeting in adaptive social cash systems is gaining increasing significance, but few governments have yet leveraged on AI technologies to reap its benefits. Hence, there is mounting pressure on social cash policymakers and practitioners to rapidly embrace the opportunities arising from AI applications, especially in times of crisis. While data and algorithmic targeting (non-AI and AI) are efficient in enrolling beneficiaries in emergency social cash systems, it may also pose serious challenges. Through a qualitative case study of an adaptive social cash programme in Pakistan, the research critically examines the data/algorithmic targeting process, and unveils the shortcomings prevalent in design, data and algorithmic decision-making that lead to certain exclusionary outcomes. The study makes several contributions to the data and policy literature. Drawing on the limitations, it first offers a set of practical recommendations for greater enrolment, and hence inclusion of beneficiaries. Second, it discusses novel opportunities that AI technologies may present in adaptive social cash systems, whilst carefully assessing the risks. Third, the study proposes an organisational AI governance framework to guide the development of responsible and ethical AI practices. The study affords policy and practical implications for governments, social cash policymakers, and practitioners in providing invaluable insights into how changing targeting practices, via AI technologies, under a governance framework can direct ethical practices that positively impacts on beneficiaries, social cash organisations, and stakeholders.

Over the past years, the concept of open research data (ORD) has gained traction as part of broader Open Science initiatives. The benefits of ORD, such as increased cost-effectiveness, transparency, and visibility, are well documented. However, researchers face barriers, which may be perceived rather than real, hindering the adoption of ORD practices. To address this challenge, we propose using ORD support services as sustainable enablers to stimulate cultural change around ORD. We engaged stakeholders across the University of Zurich and the Swiss ORD community, differentiating between researchers and ORD experts, to identify which services would best serve as sustainable enablers. After defining ORD support services and categorizing them into six key areas, we conducted surveys and interviews to gather insights on service preferences and barriers to ORD adoption. Among researchers, we identified a trend toward simpler and lower-resource services, highlighting the need for user-friendly and easily accessible support. ORD experts emphasized the importance of professional data stewardship, robust research data management (RDM) practices, and customized support to address discipline-specific needs. By combining survey and interview results, we provide a detailed overview of stakeholders’ ideas and suggestions for each proposed support area. Our study results in recommendations for academic institutions aiming to stimulate a cultural shift toward ORD. By focusing on findable, accessible, and user-friendly services, equipping researchers with fundamental RDM skills, and professionalizing data stewardship to provide customized support, institutions can foster the adoption of ORD practices. Ultimately, these measures can enhance the impact and reproducibility of scientific research.

This article critically examines how Web3 decentralization policy trends impact global digital governance, questioning whether they genuinely distribute power or merely shift influence to a new, tech-savvy elite. Based on fieldwork in Silicon Valley since August 2022 and engagement with scholars and practitioners up to December 2025, the article provides a conceptual analysis with emerging empirical insights around the nascent global Web3 movement. While Web3 advocates challenge centralized data monopolies and traditional state structures, this analysis critiques the assumption that Web3 democratizes power, highlighting both its potential for inclusion and risks of exclusion, insofar as it may reinforce hierarchies rooted in technical expertise and digital access. While acknowledging the broader landscape of Web3 governance (including hybrid and federated models) and scoping the Global North and Global South contexts considering global adoption cases, the article particularly focuses on three post-Westphalian paradigms: (i) Network States, (ii) Network Sovereignties, and (iii) Algorithmic Nations. While Network States advocate for crypto-libertarian governance, Network Sovereignties and Algorithmic Nations emphasize cooperative governance aimed at empowering minority communities, such as indigenous groups, stateless nations, and e-diasporas, through decentralized, data-driven systems. By engaging with both the limitations and some promises, prospects, and pitfalls of Web3, this article questions whether Web3 can create a more inclusive global order or if influence is increasingly concentrated among a new elite. This article contributes to debates on sovereignty, governance, and citizenship by advocating hybrid policy frameworks that balance global and local dynamics, emphasizing solidarity, digital justice, and international cooperation for equitable Web3 governance.

Quantum technologies have the potential to play a significant role in future technological and economic advancement. However, our understanding of the specific narratives and topics present in national quantum technology policies is limited, even though these policies are vital for shaping global strategies, progress, and responsible development in the field. In this study, we use narrative policy analysis together with computational topic modeling to examine 55 governmental documents from 24 countries, covering over a decade. Using BERTopic modeling and the Narrative Policy Framework, the results reveal that national initiatives primarily focus on technological leadership for security and economic prosperity, assessing technological readiness, and, to a lesser extent, commercialization, and societal impacts. Over time, we see a trend toward greater alignment in the prevalence of these narratives, with different themes beginning to be considered more equally. Nevertheless, the narrative surrounding responsible quantum development and societal implications remains the least represented. The study shows the strategic priorities of the analyzed countries and introduces an innovative method for analyzing policy texts. Based on the results, we recommend a balanced regulatory approach for quantum technologies that promotes ethical innovation, supports inclusive technological ecosystems, and encourages global collaboration. Furthermore, we caution that an excessive emphasis on leadership and competition may lead to isolated innovation systems that could hinder progress, cooperation, and joint efforts.

Following Entman’s observation that policy frames define social problems, diagnose causes and suggest remedies, we examined the strategies that 12 U.S. governors (from states matched according to population size and density, demographic composition, per capita incomes, geographic proximity, and COVID-19 incidence) used to frame COVID-19 policy agendas. After scraping the governors’ statements about COVID-19 from press releases issued from January 2020 to May 2023 (N = 14,629), we leveraged ChatGPT (GPT) to identify and assess the intensity of public health, economic stability, and civic vitality frames. Subsequent analysis explored differences in the framing strategies according to the governors’ political party and gender. In the process, this study underscores the importance of AI prompt engineering to realize GPT’s transformative potential to facilitate communication research by efficiently identifying and assessing the content of policy frames.

Analyzing topics and emotions in social media activism offers valuable insights into the competing voices that shape digital discourse. However, existing research has largely neglected the influence of geographic and linguistic diversity on public dialogue during crises. To address this gap, it is essential to recognize the varied perspectives of local communities and language groups. Doing so helps uncover specific local needs, ensures more inclusive representation, and supports the development of solutions that are responsive to the local context. We leverage machine learning models to analyze 1,036,111 public tweets from the #NoMore movement, including tweets containing #NoMore, #EthiopiaPrevails, and #SayNoMore. Our analysis examined the differences in content, emotional responses, and user influence by comparing tweets from Ethiopia and the United States (US), as well as those written in English and Amharic. The findings reveal distinct societal perspectives, emotional expressions, and opinion dynamics. Ethiopian users emphasized local issues with higher fear and joy responses, while users from the US leaned toward peace-related themes with spikes in anger. Amharic tweets focused on domestic concerns with greater emotional intensity than English tweets. These insights help surface region and language-specific perspectives often marginalized in mainstream coverage, paving the way for more inclusive and effective approaches to societal challenges.

The dynamics of information diffusion on social media platforms vary significantly between individual communities and the broader population. This study explores and compares the differences between community-based interventions and population-wide approaches in adjusting the spread of information. We first examine the temporal dynamics of social media groups, assessing their behavior through metrics such as time-dependent posts and retweets. Using functional data analysis, we investigate Twitter activities related to incidents such as the Skripal/Novichok case. We present three ways to quantify disparities between communities and uncover the strategies used by each group to promote specific narratives. We then compare the impact of targeted, community-based interventions with that of broader, population-wide responses in shaping the diffusion of information. Through this analysis, we identify key differences in how communities engage with and amplify information, revealing distinct patterns in the diffusion process. Our findings provide a comparative framework for understanding the relative consequences of different intervention strategies, offering insights into how targeted and broad approaches influence public discourse across social media platforms.

As data are becoming increasingly important resources for municipal administrations in the context of urban development, formalization of urban data governance (DG) is considered a prerequisite to systematic municipal data practice for the common good. Unlike for larger cities, it is unclear how common such formalized DG is in rural districts and small towns. We therefore mapped the current status quo in small municipalities in Germany as a case exemplifying the broader phenomenon. We systematically searched online for policy documents on DG in all metropolitan regions, all rural districts, and a quota sample of nearly a sixth of all German small towns. We then performed content analysis of the identified documents along predefined categories of urban development. Results show that hardly any small towns dispose of relevant policy documents. Rural districts are somewhat more active in formally defining DG. Identified policy documents tend to address mostly economic activities, social infrastructure, and demography, whereas Housing and Urban design and public space are among the least mentioned categories of urban development.

This review examines the legal, voluntary, and technical mechanisms that govern the ownership of nonpersonal agricultural data generated by IoT-enabled farm machinery, sensors, and related systems. Given that this data is not subject to personal data protection legislation such as General Data Protection Regulation (GDPR), its governance presents distinct challenges requiring alternative governance approaches. Drawing on 63 peer-reviewed studies published over the last decade, this review proposes an integrated conceptual framework comprising legal enforcement, voluntary governance, and technical enforcement mechanisms. A distinctive contribution of the study is to show that data ownership in agriculture becomes meaningful at the moment of data sharing, where rights claims are made visible, contested, or constrained, and that these three governance pathways must be understood jointly rather than in isolation. The analysis demonstrates that although farmers generate vast quantities of nonpersonal data, no existing legal framework explicitly grants them ownership, leaving ownership to be ambiguously allocated or de facto transferred through contracts in ways that limit their ability to contest access or downstream use. Technical mechanisms promise automated enforcement and accountability but risk codifying existing power asymmetries when the encoded rules reflect opaque or exclusionary terms. We argue for a shift from “ownership” to “data sovereignty” understood as the sustained capacity to define, monitor, and revoke conditions of data use. Achieving this requires three interlinked pillars: enforceable baseline access and use rights for farmers, accessible and preferably open-source technical infrastructure, and participatory governance arrangements.

Governments across the world are leveraging artificial intelligence (AI) to render services to citizens. Emerging economies are not left behind in this transformation but remain a gaping distance behind in their integration into public-sector service delivery compared to the private sector. To ensure the effective integration of AI services by government agencies to serve citizens, it is necessary to understand the constellation of factors driving user adoption of AI. Therefore, this study answers the question: how can government-initiated AI services be successfully accepted by citizens? Leveraging non-probability sampling, a snowball sample of 245 tertiary student-workers from across Ghana was surveyed to solicit their knowledge, attitudes, readiness, and use intentions towards AI-enabled government services. The data were analysed using FsQCA and complemented by PLS-SEM to confirm the findings. The findings reveal four unique configurations, summarised into two broad groups; AI enthusiasts and AI sceptics that drive AI adoption in government services. Positive readiness factors, such as knowledge of AI and optimism towards AI, characterise AI enthusiasts. In contrast, those described as AI sceptics still adopt government AI services despite their reservations and general distrust. AI sceptics are a delicate group that sit at the boundary between adoption and rejection, requiring special attention and strategies to orient them towards adoption. The study recommends effective education and trust-building strategies to foster AI adoption in government services. The findings are essential for driving the efficient implementation of AI-enabled services among working-class citizens in emerging economies.

Narratives shape public perceptions and policymaking around emerging technologies like quantum technologies (QTs), yet what narratives develop across different societal domains remains underexplored. This study analyzes narratives about QTs in 36 government documents, 163 business reports, and 2023 media articles published over the past 23 years, using a mixed-methods approach that combines topic modeling with qualitative thematic analysis. We find that the dystopian or utopian extremes associated with technologies such as artificial intelligence are largely absent from discourse about QTs. Media coverage tends to cover a broad range of topics, while business and government narratives emphasize technical milestones, economic competitiveness, and national security, frequently at the expense of questions about ethics, equity, and accessibility. We discuss the implications of this focus, particularly the risk that an emphasis on zero-sum geopolitical competition could foster a more closed and fragmented innovation ecosystem.

Blockchain technology has attracted attention from public sector agencies, mainly for its perceived potential to improve transparency, data integrity, and administrative processes. However, its concrete value and applicability within government settings remain contested, and real-world adoption has been limited and uneven. This raises questions regarding the conditions that promote or impede adoption at the institutional level. Fuzzy-set qualitative comparative analysis is employed in this research to explore how the combined effects of national-level regulatory clarity, financial provision, digital readiness, and ecosystem engagement shape patterns of blockchain adoption in the European public sector. Rather than identifying any single factor as decisive, our findings reveal a plurality of institutional paths leading to high adoption intensity, with regulatory certainty and European Union funding appearing most frequently on high-consistency paths. In contrast, digital readiness indicators and national research and development budgets are substitutable, challenging resource-based perceptions of technology adoption and supporting a configurational understanding that accounts for institutional interdependence and contextuality. We argue that policy strategies cannot look for overall readiness but should place key institutional strengths relative to local conditions and public value objectives.

This article examines the governance challenges of human genomic data sharing. The analysis builds upon the unique characteristics that distinguish genomic data from other forms of personal data, particularly its dual nature as both uniquely identifiable to individuals and inherently collective, reflecting familial and ethnic group characteristics. This duality informs a tripartite risk taxonomy: individual privacy violations, group-level harms, and bioterrorism threats. Examining regulatory frameworks in the European Union (EU) and China, the article demonstrates how current data protection mechanisms—primarily anonymisation and informed consent—prove inadequate for genomic data governance due to the impossibility of true anonymisation and the limitations of consent-based models in addressing the risks of such sharing. Drawing on the concept of “genomic contextualism,” the article proposes a nuanced framework that incorporates interest balancing, comprehensive data lifecycle management, and tailored technical safeguards. The objective is to protect individuals and underrepresented groups while maximising the scientific and clinical value of genomic data.